| 1. |
- Hjalmarsson, Håkan, 1963, et al.
(författare)
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A Mathematica Toolbox for Signals, Systems and Identification System Identification
- 2012
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Ingår i: IFAC Proceedings Volumes (IFAC-PapersOnline). - 1474-6670. - 9783902823069 ; 16:1, s. 1541-1546
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Konferensbidrag (refereegranskat)abstract
- In this contribution we provide a status report for the Mathematica toolbox that is described in Sjöberg 2008. The toolbox covers a comprehensive set of functions for handling deterministic and stochastic signals and models. On top of this the toolbox provides signal processing and system identification methods ranging from non-parametric to parametric, and from linear models to a wide class of non-linear models. Algorithms are tailored to be able to efficiently handle large scale data sets and models as well as symbolic computations. This allows theory to be handled alongside practice, implying that the toolbox provides an environment suitable both for education and data processing. In regards to system identification, one of the novel features is graphical support for building block-based nonlinear models. Another novel feature is that modeling errors can be propagated through applications.
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| 2. |
- Hjalmarsson, Håkan, 1962-, et al.
(författare)
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On Neural Network Model Structure in System Identification
- 1996
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Ingår i: Identification, Adaptation, Learning. The Science of Learning Models from Data. - Linköping : Linköping University Electronic Press. ; , s. 366-399
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Rapport (övrigt vetenskapligt/konstnärligt)
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| 3. |
- Juditsky, A., et al.
(författare)
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Nonlinear black-box models in system identification: Mathematical foundations
- 1995
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Ingår i: Automatica. - Linköping : Elsevier BV. - 0005-1098 .- 1873-2836. ; 31:12, s. 1725-1750
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Tidskriftsartikel (refereegranskat)abstract
- We discuss several aspects of the mathematical foundations of the nonlinear black-box identification problem. We shall see that the quality of the identification procedure is always a result of a certain trade-off between the expressive power of the model we try to identify (the larger the number of parameters used to describe the model, the more flexible is the approximation), and the stochastic error (which is proportional to the number of parameters). A consequence of this trade-off is the simple fact that a good approximation technique can be the basis of a good identification algorithm. From this point of view, we consider different approximation methods, and pay special attention to spatially adaptive approximants. We introduce wavelet and 'neuron' approximations, and show that they are spatially adaptive. Then we apply the acquired approximation experience to estimation problems. Finally, we consider some implications of these theoretical developments for the practically implemented versions of the 'spatially adaptive' algorithms. Copyright © 1995 Elsevier Science Ltd All rights reserved.
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| 4. |
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| 5. |
- Sjöberg, Jonas, 1964, et al.
(författare)
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A System, Signals and Identification Toolbox in {M}athematica with Symbolic Capabilities
- 2009
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Ingår i: 15th IFAC Symposium on System Identification, SYSID 2009. - 1474-6670. - 9783902661470 ; , s. 747-751
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Konferensbidrag (refereegranskat)abstract
- In this contribution we describe a new signals, systems and identification toolbox for the symbolic and numerical computation system Mathematica. The toolbox provides functionality for computation of properties of systems and signals ranging from frequency responses, zeros and poles to signal spectra and spectral factorizations. It also includes a wide range of identification algorithms ranging from spectral analysis to subspace and prediction error identification of models for non-linear systems. The symbolic capabilities of Mathematica are used to allow the user to construct very general model structures, and for pre-processing, such as gradient calculations, when optimizing the parameters in such structures.
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| 6. |
- Sjöberg, Jonas, et al.
(författare)
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Neural Networks in System Identification
- 1994
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Ingår i: Proc. IFAC/IFORS Symposium on Identification and System Parameter Estimation. ; , s. 2-049
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Konferensbidrag (refereegranskat)
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| 7. |
- Sjöberg, Jonas, et al.
(författare)
-
Neural Networks in System Identification
- 1994
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Ingår i: Proceedings of the 10th IFAC Symposium on System Identification. - Linköping : Linköping University. - 9780080422251 ; , s. 49-72
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Konferensbidrag (refereegranskat)abstract
- Neural Networks are non-linear black-box model structures, to be used with conventional parameter estimation methods. They have good general approximation capabilities for reasonable non-linear systems. When estimating the parameters in these structures, there is also good adaptability to concentrate on those parameters that have the most importance for the particular data set.
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| 8. |
- Sjöberg, Jonas, et al.
(författare)
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Nonlinear black-box modeling in system identification: A unified overview
- 1995
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Ingår i: Automatica. - Linköping : Elsevier BV. - 0005-1098 .- 1873-2836. ; 31:12, s. 1691-1724
-
Tidskriftsartikel (refereegranskat)abstract
- A nonlinear black-box structure for a dynamical system is a model structure that is prepared to describe virtually any nonlinear dynamics. There has been considerable recent interest in this area, with structures based on neural networks, radial basis networks, wavelet networks and hinging hyperplanes, as well as wavelet-transform-based methods and models based on fuzzy sets and fuzzy rules. This paper describes all these approaches in a common framework, from a user's perspective. It focuses on what are the common features in the different approaches, the choices that have to be made and what considerations are relevant for a successful system-identification application of these techniques. It is pointed out that the nonlinear structures can be seen as a concatenation of a mapping form observed data to a regression vector and a nonlinear mapping from the regressor space to the output space. These mappings are discussed separately. The latter mapping is usually formed as a basis function expansion. The basis functions are typically formed from one simple scalar function, which is modified in terms of scale and location. The expansion from the scalar argument to the regressor space is achieved by a radial- or a ridge-type approach. Basic techniques for estimating the parameters in the structures are criterion minimization, as well as two-step procedures, where first the relevant basis functions are determined, using data, and then a linear least-squares step to determine the coordinates of the function approximation. A particular problem is to deal with the large number of potentially necessary parameters. This is handled by making the number of 'used' parameters considerably less than the number of 'offered' parameters, by regularization, shrinking, pruning or regressor selection. Copyright © 1995 Elsevier Science Ltd All rights reserved.
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